Update app.py

app.py

@@ -5,7 +5,6 @@ import numpy as np
 from huggingface_hub import hf_hub_download
 
 # --- MODEL LOADING ---
-# Fetches the model directly from your Hugging Face model repository
 REPO_ID = "P-Mishra/engine-predictive-maintenance"
 MODEL_FILENAME = "rf_predictive_maintenance.pkl"
 
@@ -23,55 +22,69 @@ model = load_model()
 # --- UI SETUP ---
 st.set_page_config(page_title="Engine Health Monitor", page_icon="🚢")
 st.title("🚢 Engine Predictive Maintenance")
-st.write("Input sensor data to predict potential engine failure.")
+st.write("Professional Monitoring System for Engine Health")
 
 # --- USER INPUTS ---
 col1, col2 = st.columns(2)
 
 with col1:
     engine_rpm = st.number_input("Engine RPM", value=1200.0)
-    lub_oil_temp = st.number_input("Lubricating Oil Temp (°C)", value=85.0)
     lub_oil_pressure = st.number_input("Lubricating Oil Pressure (bar)", value=4.5)
+    fuel_pressure = st.number_input("Fuel Pressure (bar)", value=5.0)
+    coolant_pressure = st.number_input("Coolant Pressure (bar)", value=2.5)
 
 with col2:
-    fuel_pressure = st.number_input("Fuel Pressure (bar)", value=5.0)
+    lub_oil_temp = st.number_input("Lubricating Oil Temp (°C)", value=85.0)
     coolant_temp = st.number_input("Coolant Temp (°C)", value=80.0)
-    coolant_pressure = st.number_input("Coolant Pressure (bar)", value=2.5)
 
-# --- FEATURE ENGINEERING (Must match notebook logic) ---
+# --- FEATURE ENGINEERING ---
 eps = 1e-6
 coolant_temp_pressure_interaction = coolant_temp * coolant_pressure
 coolant_temp_pressure_ratio = coolant_temp / (coolant_pressure + eps)
 lub_oil_temp_engine_rpm_interaction = lub_oil_temp * engine_rpm
 fuel_pressure_engine_rpm_ratio = fuel_pressure / (engine_rpm + eps)
 
-# --- DATAFRAME CONSTRUCTION ---
-# Important: Features must be in the exact order they were trained in your notebook
+# --- DATAFRAME CONSTRUCTION (VERIFIED ORDER) ---
+# This list matches your model's 'feature_names_in_' exactly.
 feature_columns = [
-    'engine_rpm', 'lub_oil_temp', 'lub_oil_pressure', 'fuel_pressure',
-    'coolant_temp', 'coolant_pressure', 'coolant_temp_pressure_interaction',
-    'coolant_temp_pressure_ratio', 'lub_oil_temp_engine_rpm_interaction',
+    'engine_rpm', 
+    'lub_oil_pressure', 
+    'fuel_pressure', 
+    'coolant_pressure', 
+    'lub_oil_temp', 
+    'coolant_temp', 
+    'coolant_temp_pressure_interaction', 
+    'coolant_temp_pressure_ratio', 
+    'lub_oil_temp_engine_rpm_interaction', 
     'fuel_pressure_engine_rpm_ratio'
 ]
 
 input_data = pd.DataFrame([[
-    engine_rpm, lub_oil_temp, lub_oil_pressure, fuel_pressure, 
-    coolant_temp, coolant_pressure, coolant_temp_pressure_interaction,
-    coolant_temp_pressure_ratio, lub_oil_temp_engine_rpm_interaction,
+    engine_rpm, 
+    lub_oil_pressure, 
+    fuel_pressure, 
+    coolant_pressure, 
+    lub_oil_temp, 
+    coolant_temp, 
+    coolant_temp_pressure_interaction, 
+    coolant_temp_pressure_ratio, 
+    lub_oil_temp_engine_rpm_interaction, 
     fuel_pressure_engine_rpm_ratio
 ]], columns=feature_columns)
 
 # --- PREDICTION ---
-if st.button("Run Diagnostic"):
+if st.button("Analyze Engine Condition"):
     if model is not None:
-        prediction = model.predict(input_data)
-        
-        st.divider()
-        if prediction[0] == 1:
-            st.error("### 🚨 Result: Maintenance Required")
-            st.write("The model has detected patterns indicating a high probability of engine failure.")
-        else:
-            st.success("### ✅ Result: Normal Operation")
-            st.write("Engine sensors are within safe operating parameters.")
+        try:
+            prediction = model.predict(input_data)
+            st.divider()
+            if prediction[0] == 1:
+                st.error("### 🚨 Result: Maintenance Required")
+                st.write("High failure risk detected based on sensor interaction patterns.")
+            else:
+                st.success("### ✅ Result: Normal Operation")
+                st.write("Engine is operating within safe nominal parameters.")
+        except Exception as e:
+            st.error(f"Prediction Error: {e}")
     else:
-        st.error("Model not available.")
+        st.error("Model not loaded correctly.")